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ckrao.wordpress.com | ||
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algorithmsoup.wordpress.com
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| | | | | The ``probabilistic method'' is the art of applying probabilistic thinking to non-probabilistic problems. Applications of the probabilistic method often feel like magic. Here is my favorite example: Theorem (Erdös, 1965). Call a set $latex {X}&fg=000000$ sum-free if for all $latex {a, b \in X}&fg=000000$, we have $latex {a + b \not\in X}&fg=000000$. For any finite... | |
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statisticaloddsandends.wordpress.com
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| | | | | If $latex Z_1, \dots, Z_n$ are independent $latex \text{Cauchy}(0, 1)$ variables and $latex w= (w_1, \dots, w_n)$ is a random vector independent of the $latex Z_i$'s with $latex w_i \geq 0$ for all $latex i$ and $latex w_1 + \dots w_n = 0$, it is well-known that $latex \displaystyle\sum_{i=1}^n w_i Z_i$ also has a $latex... | |
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thatsmaths.com
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| | | | | We are all familiar with Pascal's Triangle, also known as the Arithmetic Triangle (AT). Each entry in the AT is the sum of the two closest entries in the row above it. The $latex {k}&fg=000000$-th entry in row $latex {n}&fg=000000$ is the binomial coefficient $latex {\binom{n}{k}}&fg=000000$ (read $latex {n}&fg=000000$-choose-$latex {k}&fg=000000$), the number of ways of... | |
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michael-lewis.com
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| | | This is a short summary of some of the terminology used in machine learning, with an emphasis on neural networks. I've put it together primarily to help my own understanding, phrasing it largely in non-mathematical terms. As such it may be of use to others who come from more of a programming than a mathematical background. | ||
